Cognitive Medium Access: Exploration, Exploitation and Competition
Lifeng Lai, Hesham El Gamal, Hai Jiang, H. Vincent Poor

TL;DR
This paper models cognitive medium access as a multi-armed bandit problem, deriving optimal strategies for single and multiple users to efficiently explore and exploit spectrum opportunities without prior traffic knowledge.
Contribution
It introduces a Bayesian approach to derive optimal and low-complexity medium access strategies for both single and multiple cognitive users, extending to multi-channel sensing scenarios.
Findings
Optimal Bayesian strategy derived for single-user case.
Low complexity asymptotically optimal strategies developed.
Protocols effectively balance exploration and exploitation in competitive environments.
Abstract
This paper establishes the equivalence between cognitive medium access and the competitive multi-armed bandit problem. First, the scenario in which a single cognitive user wishes to opportunistically exploit the availability of empty frequency bands in the spectrum with multiple bands is considered. In this scenario, the availability probability of each channel is unknown to the cognitive user a priori. Hence efficient medium access strategies must strike a balance between exploring the availability of other free channels and exploiting the opportunities identified thus far. By adopting a Bayesian approach for this classical bandit problem, the optimal medium access strategy is derived and its underlying recursive structure is illustrated via examples. To avoid the prohibitive computational complexity of the optimal strategy, a low complexity asymptotically optimal strategy is…
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Taxonomy
TopicsCognitive Radio Networks and Spectrum Sensing · Advanced Bandit Algorithms Research · Age of Information Optimization
